Role: Credit eTrading Quantitative Researcher (VP)
Firm: Tier 1 US Investment Bank
Compensation: $350K-$400K total compensation
Location: New York
Role Overview
A leading Tier 1 US Investment Bank is seeking to expand its Global Credit E-Trading business, a premier provider of market-making services in bonds, credit derivatives, and structured financial products. The business caters to a diverse client base, including banks, insurance firms, mutual funds, and hedge funds, by offering liquidity and innovative financial solutions. Collaborative teams of traders, sales professionals, and research analysts work together to deliver market insights and opportunities to clients.
The Team
The Credit Quantitative Research Algorithmic team partners closely with traders to support Credit flow products such as Corporate Bonds, Municipal Bonds, Leveraged Loans, and Credit Indices.
This team employs a quantitative approach to trading, leveraging expertise in market microstructure and advanced data analytics to build mid-price and reference price models. These models underpin the Credit Systematic Market Making initiative. Additionally, the team enhances trader workflows with data-driven insights and maintains a real-time analytics platform for various credit flow products.
The Credit Quantitative Research Algorithmic team partners closely with traders to support Credit flow products such as Corporate Bonds, Municipal Bonds, Leveraged Loans, and Credit Indices.
This team employs a quantitative approach to trading, leveraging expertise in market microstructure and advanced data analytics to build mid-price and reference price models. These models underpin the Credit Systematic Market Making initiative. Additionally, the team enhances trader workflows with data-driven insights and maintains a real-time analytics platform for various credit flow products.
The group operates within the broader Quantitative Research division, which is responsible for delivering models and analytics to optimize trading and risk management across the Markets business. Their work spans model development, implementation, regulatory approvals, production integration, and ongoing support.
Position Opportunity
This role offers the chance to join the Credit Quantitative Research Algorithmic team in New York as a Vice President. The primary focus will be on developing and implementing real-time analytics and mid-price/reference models for corporate bonds. Candidates must have direct experience in a Credit eTrading Quant Research role.
This role offers the chance to join the Credit Quantitative Research Algorithmic team in New York as a Vice President. The primary focus will be on developing and implementing real-time analytics and mid-price/reference models for corporate bonds. Candidates must have direct experience in a Credit eTrading Quant Research role.
Key Responsibilities
- Leverage statistical methods and data science to design and implement data-driven mid/reference price models.
- Develop, deploy, and maintain systems delivering real-time pricing models for corporate bonds and loans.
- Introduce cutting-edge tools and technologies to enhance trading desk capabilities.
- Collaborate with Technology teams to improve data collection, intelligence, and analytical processes.
- Conduct research, back-testing, and reporting to refine and validate pricing and market-making models.
Required Qualifications
The ideal candidate must have demonstrated experience in a Credit eTrading Quant Research role, thrive in a fast-paced environment, communicate effectively with trading, technology, and control teams, and value sound software design principles.
The ideal candidate must have demonstrated experience in a Credit eTrading Quant Research role, thrive in a fast-paced environment, communicate effectively with trading, technology, and control teams, and value sound software design principles.
Essential Skills and Experience
- Proven experience in quantitative research specifically for Credit eTrading, with a focus on corporate bonds, credit indices, or related products.
- Advanced expertise in data science, including statistics, probability, and machine learning techniques (e.g., parameter optimization, regularization, neural networks, Gaussian processes).
- Hands-on experience with real-world data analysis in financial markets.
- Proficiency in object-oriented programming (e.g., C++, Java) and extensive Python expertise, including libraries like pandas, NumPy, scikit-learn, TensorFlow, and Spark.
- Experience in reactive programming is a plus.
- Strong attention to detail and commitment to delivering production-quality analytics.
- Exceptional problem-solving skills and the ability to synthesize findings for trading and management.
- Capacity to perform under pressure in a high-stakes environment.
- A proactive approach to learning about financial markets, quantitative models, and technology platforms.
Educational Requirements
A graduate degree in a STEM discipline is mandatory.
